import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
import numpy as np
import os
import sys
import pickle
import seaborn as sns
sns.set_theme(style="whitegrid")
import warnings
warnings.filterwarnings("ignore")
The divergence of the results from reality is due to an overuse of nuclear power (either because EDF has done load following which is not modelled by the programme because nuclear power is too cheap, or because the ENTSOE data gives too much availability) as well as to the non-modelling of the Netherlands and Austria
#Run this for simulation
year=2017 #must be between 2017 and 2019
number_of_sub_techs=5
consumption=pd.read_csv('Input data/'+str(year)+'/'+str(year)+'_MultiNodeAreaConsumption.csv')
if os.path.basename(os.getcwd()) == "Seven_node_Europe":
os.chdir('../..')
from Main_historical import *
Variables=main_historical(year,number_of_sub_techs)
Model creation at 0:00:01.126538 Start solving at 0:06:43.273729 Solved at 0:14:41.933736 Total duration: 0:14:46.614648 Model creation at 0:00:01.053781 Start solving at 0:07:07.677386 Solved at 0:15:06.281685 Total duration: 0:15:12.020071
#Run this to load a result file
year=2019 #must be between 2017 and 2019
consumption=pd.read_csv('Input data/'+str(year)+'/'+str(year)+'_MultiNodeAreaConsumption.csv')
with open("Result_"+str(year)+".pickle", 'rb') as f:
Variables=pickle.load(f)
states=["Historical"]
exch_list=[Variables["exchange"]]
data=[Variables["energy"]]
data_stor=[Variables["storageOut"]]
data_capa=[Variables["capacity"]]
data_stor_capa=[Variables["Pmax"]]
colors=["black"]
#subtechs list
l=data_capa[0]
l=l.TECHNOLOGIES.unique()
u=[]
n_list=[]
for tech_name in l:
if tech_name[:4]=="Coal" and tech_name!="Coal":
n_list.append(tech_name[4:])
# total_consum=round(consum.areaConsumption.sum()/10**6,2)
tech_list=["OldNuke","NewNuke","Coal","Lignite","CCG","TAC","HydroRiver","HydroReservoir",'HydroStorage',"Biomass",
"WindOnShore","WindOffShore","Solar","Battery"]
df_capa=pd.DataFrame(columns=["TECHNOLOGIES"]+states)
df_capa["TECHNOLOGIES"]=np.array(tech_list)
# print(len(data))
for i in range(len(data)):
capa=data_capa[i]
for t in tech_list:
t_list=[t]
if len(n_list)>0:
for j in range(len(n_list)):
t_list.append(t+n_list[j])
if t not in ["HydroStorage","Battery"] :
val=round(capa[capa.TECHNOLOGIES.isin(t_list)].capacity.to_numpy().sum()/10**3,2)
# print(val)
df_capa.loc[df_capa.TECHNOLOGIES==t,states[i]]=val
else:
u=data_stor_capa[i]
val=round(u[u.STOCK_TECHNO==t].Pmax.to_numpy().sum()/10**3,2)
# print(val)
df_capa.loc[df_capa.TECHNOLOGIES==t,states[i]]=val
total_capa=df_capa[states[0]].sum()
df_capa
| TECHNOLOGIES | Historical | |
|---|---|---|
| 0 | OldNuke | 98.31 |
| 1 | NewNuke | 0.0 |
| 2 | Coal | 51.5 |
| 3 | Lignite | 21.2 |
| 4 | CCG | 147.11 |
| 5 | TAC | 14.4 |
| 6 | HydroRiver | 29.44 |
| 7 | HydroReservoir | 38.0 |
| 8 | HydroStorage | 38.36 |
| 9 | Biomass | 16.71 |
| 10 | WindOnShore | 114.94 |
| 11 | WindOffShore | 17.32 |
| 12 | Solar | 100.44 |
| 13 | Battery | 0.0 |
tech_list=["OldNuke","NewNuke","Coal","Lignite","CCG","TAC","HydroRiver","HydroReservoir",'HydroStorage',"Biomass",
"WindOnShore","WindOffShore","Solar","Battery"]
for area in ["FR","DE","GB","BE","IT","ES","CH"]:
print("\nCapacities installed for "+area)
df_capa=pd.DataFrame(columns=["TECHNOLOGIES"]+states)
df_capa["TECHNOLOGIES"]=np.array(tech_list)
# print(len(data))
for i in range(len(data)):
capa=data_capa[i]
capa=capa[capa.AREAS==area]
for t in tech_list:
t_list=[t]
if len(n_list)>0:
for j in range(len(n_list)):
t_list.append(t+n_list[j])
if t not in ["HydroStorage","Battery"] :
val=round(capa[capa.TECHNOLOGIES.isin(t_list)].capacity.to_numpy().sum()/10**3,2)
# print(val)
df_capa.loc[df_capa.TECHNOLOGIES==t,states[i]]=val
else:
u=data_stor_capa[i]
u=u[u.AREAS==area]
val=round(u[u.STOCK_TECHNO==t].Pmax.to_numpy().sum()/10**3,2)
# print(val)
df_capa.loc[df_capa.TECHNOLOGIES==t,states[i]]=val
print(df_capa)
Capacities installed for FR
TECHNOLOGIES Historical
0 OldNuke 63.13
1 NewNuke 0.0
2 Coal 3.0
3 Lignite 0.0
4 CCG 6.58
5 TAC 0.7
6 HydroRiver 10.96
7 HydroReservoir 8.28
8 HydroStorage 5.02
9 Biomass 1.93
10 WindOnShore 13.61
11 WindOffShore 0.0
12 Solar 8.19
13 Battery 0.0
Capacities installed for DE
TECHNOLOGIES Historical
0 OldNuke 9.52
1 NewNuke 0.0
2 Coal 25.29
3 Lignite 21.2
4 CCG 17.11
5 TAC 11.42
6 HydroRiver 3.98
7 HydroReservoir 1.3
8 HydroStorage 9.42
9 Biomass 7.75
10 WindOnShore 52.79
11 WindOffShore 6.39
12 Solar 45.3
13 Battery 0.0
Capacities installed for GB
TECHNOLOGIES Historical
0 OldNuke 9.23
1 NewNuke 0.0
2 Coal 8.79
3 Lignite 0.0
4 CCG 42.2
5 TAC 0.0
6 HydroRiver 1.88
7 HydroReservoir 0.0
8 HydroStorage 2.74
9 Biomass 4.06
10 WindOnShore 13.63
11 WindOffShore 9.38
12 Solar 13.44
13 Battery 0.0
Capacities installed for BE
TECHNOLOGIES Historical
0 OldNuke 5.94
1 NewNuke 0.0
2 Coal 0.0
3 Lignite 0.0
4 CCG 3.01
5 TAC 2.27
6 HydroRiver 0.17
7 HydroReservoir 0.0
8 HydroStorage 1.31
9 Biomass 0.62
10 WindOnShore 2.25
11 WindOffShore 1.55
12 Solar 3.37
13 Battery 0.0
Capacities installed for IT
TECHNOLOGIES Historical
0 OldNuke 0.0
1 NewNuke 0.0
2 Coal 4.85
3 Lignite 0.0
4 CCG 47.31
5 TAC 0.0
6 HydroRiver 10.65
7 HydroReservoir 3.86
8 HydroStorage 7.57
9 Biomass 1.54
10 WindOnShore 9.62
11 WindOffShore 0.0
12 Solar 20.87
13 Battery 0.0
Capacities installed for ES
TECHNOLOGIES Historical
0 OldNuke 7.12
1 NewNuke 0.0
2 Coal 9.56
3 Lignite 0.0
4 CCG 30.38
5 TAC 0.0
6 HydroRiver 1.16
7 HydroReservoir 19.15
8 HydroStorage 5.65
9 Biomass 0.51
10 WindOnShore 22.96
11 WindOffShore 0.0
12 Solar 6.75
13 Battery 0.0
Capacities installed for CH
TECHNOLOGIES Historical
0 OldNuke 3.37
1 NewNuke 0.0
2 Coal 0.0
3 Lignite 0.0
4 CCG 0.5
5 TAC 0.0
6 HydroRiver 0.63
7 HydroReservoir 5.42
8 HydroStorage 6.64
9 Biomass 0.3
10 WindOnShore 0.08
11 WindOffShore 0.0
12 Solar 2.52
13 Battery 0.0
country="FR"
p=Variables["capacity"]
p=p[p.AREAS==country]
p
| AREAS | TECHNOLOGIES | capacity | |
|---|---|---|---|
| 0 | FR | Coal6666666666666666 | 374.625 |
| 1 | FR | curtailment | 20000.000 |
| 2 | FR | CCG3333333333333333 | 823.125 |
| 3 | FR | CCG8333333333333333 | 823.125 |
| 4 | FR | Lignite3333333333333333 | 0.000 |
| 5 | FR | Lignite16666666666666666 | 0.000 |
| 6 | FR | Lignite6666666666666666 | 0.000 |
| 7 | FR | Biomass3333333333333333 | 241.375 |
| 8 | FR | TAC8333333333333333 | 87.875 |
| 9 | FR | TAC | 351.500 |
| 10 | FR | Biomass8333333333333333 | 241.375 |
| 11 | FR | CCG16666666666666666 | 823.125 |
| 12 | FR | Lignite8333333333333333 | 0.000 |
| 13 | FR | WindOnShore | 13610.000 |
| 14 | FR | Lignite | 0.000 |
| 15 | FR | Coal16666666666666666 | 374.625 |
| 16 | FR | NewNuke | 0.000 |
| 17 | FR | Coal3333333333333333 | 374.625 |
| 18 | FR | WindOffShore | 0.000 |
| 19 | FR | Coal8333333333333333 | 374.625 |
| 20 | FR | CCG6666666666666666 | 823.125 |
| 21 | FR | TAC16666666666666666 | 87.875 |
| 22 | FR | OldNuke | 63130.000 |
| 23 | FR | HydroReservoir | 8279.000 |
| 24 | FR | TAC6666666666666666 | 87.875 |
| 25 | FR | Biomass16666666666666666 | 241.375 |
| 26 | FR | Biomass6666666666666666 | 241.375 |
| 27 | FR | Solar | 8188.000 |
| 28 | FR | HydroRiver | 10955.000 |
| 29 | FR | TAC3333333333333333 | 87.875 |
| 30 | FR | Biomass | 965.500 |
| 31 | FR | CCG | 3292.500 |
| 32 | FR | Coal | 1498.500 |
# total_consum=round(consum.areaConsumption.sum()/10**6,2)
tech_list=["OldNuke","NewNuke","Coal","Lignite","CCG","TAC","HydroRiver","HydroReservoir",'HydroStorage',"Biomass",
"WindOnShore","WindOffShore","Solar","Battery","curtailment"]
df_prod=pd.DataFrame(columns=["TECHNOLOGIES"]+states)
df_prod["TECHNOLOGIES"]=np.array(tech_list)
for i in range(len(data)):
prod=data[i]
for t in tech_list:
t_list=[t]
if len(n_list)>0:
for j in range(len(n_list)):
t_list.append(t+n_list[j])
if t!="HydroStorage":
val=round(prod[prod.TECHNOLOGIES.isin(t_list)].energy.to_numpy().sum()/10**6,2)
# print(val)
df_prod.loc[df_prod.TECHNOLOGIES==t,states[i]]=val
else:
u=data_stor[i]
val=round(u[u.STOCK_TECHNO==t].storageOut.to_numpy().sum()/10**6,2)
df_prod.loc[df_prod.TECHNOLOGIES==t,states[i]]=val
total_prod=df_prod[states[0]].sum()
df_prod_bar=pd.DataFrame(columns=["TECHNOLOGIES","Production (TWh)","Production (%)","Scenario"])
for c in df_prod.columns[1:]:
u=df_prod[["TECHNOLOGIES",c]]
u.rename(columns={c:"Production (TWh)"},inplace=True)
u["Production (%)"]=u["Production (TWh)"]/u["Production (TWh)"].sum()*100
u["Scenario"]=c
df_prod_bar=pd.concat([df_prod_bar,u],ignore_index=True)
import plotly.express as px
fig = px.bar(df_prod_bar, x='Scenario', y='Production (%)', color="TECHNOLOGIES")
fig.show()
fig = px.pie(df_prod_bar, values='Production (TWh)', names='TECHNOLOGIES', title='Production per technology (in %)')
fig.show()
# total_consum=round(consum.areaConsumption.sum()/10**6,2)
countries=["FR","DE","BE","GB","IT","CH","ES"]
tech_list=["OldNuke","NewNuke","Biomass","Coal","Lignite","CCG","TAC","HydroRiver","HydroReservoir",'HydroStorage','Battery',
"WindOnShore","WindOffShore","Solar"]
df_prod_country=pd.DataFrame(columns=["AREAS","TECHNOLOGIES","State","Production (TWh)"])
for i in range(len(states)):
prod=data[i]
for c in countries:
for t in tech_list:
t_list=[t]
if len(n_list)>0:
for j in range(len(n_list)):
t_list.append(t+n_list[j])
if t not in ["HydroStorage","Battery"]:
val=round(prod[(prod.TECHNOLOGIES.isin(t_list)) & (prod.AREAS==c)].energy.to_numpy().sum()/10**6,2)
array=np.array([[c,t,states[i],val]])
df_prod_country=pd.concat([df_prod_country,
pd.DataFrame(array,columns=["AREAS","TECHNOLOGIES","State","Production (TWh)"])])
else:
u=data_stor[i]
val=round(u[(u.STOCK_TECHNO==t) & (u.AREAS==c)].storageOut.to_numpy().sum()/10**6,2)
array=np.array([[c,t,states[i],val]])
df_prod_country=pd.concat([df_prod_country,
pd.DataFrame(array,columns=["AREAS","TECHNOLOGIES","State","Production (TWh)"])])
df_prod_country["Production (TWh)"]=df_prod_country["Production (TWh)"].astype('float')
# comparison_per_country=df_prod_country.copy()
# for country in countries:
# total_prod=df_prod[states[0]].sum()
# total_consum=round(consum[consum.AREAS==country].areaConsumption.sum()/10**6,2)
# comparison_per_country.loc[comparison_per_country.AREAS==country,"Production (TWh)"]= comparison_per_country.loc[comparison_per_country.AREAS==country,"Production (TWh)"].astype(float)/total_consum
# comparison_per_country["Difference with historical"]=np.zeros(len(comparison_per_country))
# for state in states:
# comparison_per_country.loc[comparison_per_country.State==state,"Difference with "+states[0]]=(comparison_per_country.loc[comparison_per_country.State==state,"Production (TWh)"]-comparison_per_country.loc[comparison_per_country.State=="Historical","Production (TWh)"])*100
from matplotlib.ticker import PercentFormatter
g = sns.catplot(x="State", y="Production (TWh)",hue="AREAS",col="TECHNOLOGIES",col_wrap=3,kind="bar",
data=df_prod_country)
# for ax in g.axes.flat:
# ax.yaxis.set_major_formatter(PercentFormatter())
plt.show()
country="FR"
p=Variables["energy"]
p=p[p.AREAS==country]
storout=Variables["storageOut"]
#print("HydroStorage",round(storout[storout.AREAS==country].storageOut.sum()/10**6,2),"TWh")
storout=storout[(storout.AREAS==country) & (storout.STOCK_TECHNO!="Battery")]
storout=storout.rename(columns={'STOCK_TECHNO':'TECHNOLOGIES',"storageOut":"energy"})
p=pd.concat([p,storout],ignore_index=True)
tot=0
for t in p.TECHNOLOGIES.unique():
l=p[p.TECHNOLOGIES==t]
print(t,round(l.energy.sum()/10**6,2),"TWh")
tot+=l.energy.sum()
print("Total produced",round(tot/10**6,2),"TWh")
Coal6666666666666666 1.58 TWh curtailment 0.0 TWh CCG3333333333333333 2.92 TWh CCG8333333333333333 0.0 TWh Lignite3333333333333333 0.0 TWh Lignite16666666666666666 0.0 TWh Lignite6666666666666666 0.0 TWh Biomass3333333333333333 0.0 TWh TAC8333333333333333 0.0 TWh TAC 0.0 TWh Biomass8333333333333333 0.0 TWh CCG16666666666666666 3.58 TWh Lignite8333333333333333 0.0 TWh WindOnShore 32.71 TWh Lignite 0.0 TWh Coal16666666666666666 2.16 TWh NewNuke 0.0 TWh Coal3333333333333333 2.05 TWh WindOffShore 0.0 TWh Coal8333333333333333 1.4 TWh CCG6666666666666666 0.04 TWh TAC16666666666666666 0.0 TWh OldNuke 393.39 TWh HydroReservoir 17.39 TWh TAC6666666666666666 0.0 TWh Biomass16666666666666666 0.0 TWh Biomass6666666666666666 0.0 TWh Solar 11.42 TWh HydroRiver 40.44 TWh TAC3333333333333333 0.0 TWh Biomass 0.0 TWh CCG 1.08 TWh Coal 4.84 TWh HydroStorage 2.56 TWh Total produced 517.56 TWh
tech_list=["OldNuke","NewNuke","Biomass","Coal","CCG","HydroRiver","HydroReservoir",'HydroStorage',
"WindOnShore","WindOffShore","Solar","curtailment"]
sorted_df=pd.DataFrame(columns=["Date","TECHNOLOGIES","energy"])
for i in range(len(tech_list)):
d=p[p.TECHNOLOGIES==tech_list[i]].sort_values(by="Date")
sorted_df=pd.concat([sorted_df,d[["Date","TECHNOLOGIES","energy"]]],ignore_index=True)
fig = px.area(sorted_df, x="Date", y="energy", color="TECHNOLOGIES",line_group="TECHNOLOGIES",title=country+" simulations")
consum=consumption[consumption.AREAS==country]
fig.add_scatter(x=consum.Date, y=consum.areaConsumption, name="Consumption",mode="lines",line_color="black")
fig.show()
stocklevels=[Variables["stockLevel"]]
for area in stocklevels[0].AREAS.unique():
fig, ax = plt.subplots(1,figsize=(20,10))
fig.suptitle(area+" Hydrostorage stock level")
v=stocklevels[0]
u=v[v.AREAS==area]
u=u[u.STOCK_TECHNO=="HydroStorage"]
ax.plot(u.Date,u.stockLevel,"o",color=colors[0],label=states[0])
ax.legend()
ax.set_ylabel("Storage (MWh)")
plt.xlabel("Date")
c1,c2="FR","ES"
plt.figure(figsize=(10,5))
fig=plt.figure(figsize=(20,5))
plt.title(c1+'-'+c2+" exchanges")
u=exch_list[0]
v=u[(u.AREAS==c1)&(u.AREAS1==c2)]
v.TIMESTAMP=pd.to_datetime(v.TIMESTAMP)
plt.scatter(v.TIMESTAMP,v.exchange,label=states[0],s=20,alpha=0.3,color=colors[0])
plt.legend()
plt.ylabel('Exchanges (MW)')
# plt.subplots_adjust(left=None, bottom=0, right=None, top=1, wspace=0.2, hspace=None)
plt.xlabel("Date")
Text(0.5, 0, 'Date')
<Figure size 720x360 with 0 Axes>
ext_fr=["DE","BE","GB","IT","CH","ES"]
exchanges_fr=pd.DataFrame(columns=["state",'countries',"Exchanges (TWh)"])
other_exchanges=pd.DataFrame(columns=["state",'countries',"Exchanges (TWh)"])
for i in range(len(exch_list)):
s=0
exchange=exch_list[i]
for c in ["DE","BE","GB","IT","CH","ES"]:
value=round(exchange[(exchange.AREAS=="FR") & (exchange.AREAS1==c)].exchange.sum()/10**6,2)
s+=value
u=pd.DataFrame(np.array([[states[i],c,
value]]),
columns=["state",'countries',"Exchanges (TWh)"])
exchanges_fr=pd.concat([exchanges_fr,u],ignore_index=True)
u=pd.DataFrame(np.array([[states[i],"Total",
s]]),
columns=["state",'countries',"Exchanges (TWh)"])
exchanges_fr=pd.concat([exchanges_fr,u],ignore_index=True)
for a,b in [["CH","DE"],["CH","IT"],["DE","IT"]]:
value=round(exchange[(exchange.AREAS==a) & (exchange.AREAS1==b)].exchange.sum()/10**6,2)
u=pd.DataFrame(np.array([[states[i],a+"-"+b,
value]]),
columns=["state",'countries',"Exchanges (TWh)"])
other_exchanges=pd.concat([other_exchanges,u],ignore_index=True)
exchanges_fr["Exchanges (TWh)"]=exchanges_fr["Exchanges (TWh)"].astype(float)
other_exchanges["Exchanges (TWh)"]=other_exchanges["Exchanges (TWh)"].astype(float)
ax = sns.barplot(x="countries", y="Exchanges (TWh)", hue="state",data=exchanges_fr)
ax.title.set_text("France exchanges")
ax = sns.barplot(x="countries", y="Exchanges (TWh)", hue="state",data=other_exchanges)
ax.title.set_text("Other exchanges")
capacityCost=np.array([Variables['capacityCosts'].capacityCosts.sum()/10**9])
energyCost=np.array([Variables['energyCosts'].energyCosts.sum()/10**9])
ax=sns.barplot(x=["Scenario"],y=capacityCost)
plt.ylabel("B€/year")
plt.title("Annualized capacity cost")
ax.bar_label(ax.containers[0])
[Text(0, 0, '102.442')]
ax=sns.barplot(x=["Scenario"],y=energyCost)
plt.ylabel("B€/year")
plt.title("Annualized energy cost")
ax.bar_label(ax.containers[0])
[Text(0, 0, '31.3012')]
ax=sns.barplot(x=["Scenario"],y=energyCost+capacityCost)
plt.ylabel("B€/year")
plt.title("Annualized total cost")
ax.bar_label(ax.containers[0])
[Text(0, 0, '133.743')]
tot_cost=energyCost+capacityCost
ax=sns.barplot(x=["Scenario"],y=tot_cost*10**9/np.array([total_prod])*10**-6)
plt.ylabel("€/MWh")
plt.title("Cost of electricity")
ax.bar_label(ax.containers[0])
[Text(0, 0, '68.0256')]
